CNVs in single cell FTE of Patient 11528

suppressPackageStartupMessages(library(HoneyBADGER))
suppressPackageStartupMessages(library(Rsamtools))
suppressPackageStartupMessages(library(SingleCellExperiment))
suppressPackageStartupMessages(library(scater))
suppressPackageStartupMessages(library(ggplot2))
load("~/OneDrive - OnTheHub - The University of Oxford/Project scRNAseq analysis/LOH/R/20180712P11528.RData")

table(cov.sc["chr17:7577515-7577515",] >0, r["chr17:7577515-7577515",] >0)

##        
##         FALSE TRUE
##   FALSE   194    0
##   TRUE     13   17

For somatic mutation chr17:7577515-7577515 in tumour: * 194 cells do not have coverage * 17 cells only have mutated allele * 13 cells only have reference allele

VAF plot for P11528 tumour

which(sapply(NR$`11528_Blood_Exm`,length) == 2)

## chr6:57398226_T/A 
##              2579

NR[2579,] <- c(30,119)
NV[2579,1:2] <- c(12,31)
blood <- unlist(NV[,1])/unlist(NR[,1])
tumour <- unlist(NV[,2])/unlist(NR[,2])
source("../../WES_analysis/R/Ahmed_functions.R")

df <- data.frame(chr = NV$chr,
                 pos = NV$pos,
                 vaf = tumour)

df <- replaceChroms(df,1)
df <- df[order( as.numeric(as.character(df[,2]))),]
df <- df[order(df[,1]),]
plot(df[,3], cex= 0.5, xaxt="n", xlab="", pch=20, las=1, col=rgb(0,0,0,alpha=0.3) , ylab = "VAF of 11528 tumour")
plotChroms(df, 1, 0.5, "blue")

In tumour sample, we can see highly confident LOH on Chromosome 2, 13, 15, 17 and 22. It also suggests that there is LOH on TP53.

df <- data.frame(chr = NV$chr,
                 pos = NV$pos,
                 vaf = blood)
df <- replaceChroms(df,1)
df <- df[order( as.numeric(as.character(df[,2]))),]
df <- df[order(df[,1]),]
plot(df[,3], cex= 0.5, xaxt="n", xlab="", pch=20, las=1, col=rgb(0,0,0,alpha=0.3) , ylab = "VAF of 11528 blood")
plotChroms(df, 1, 0.5, "blue")

df <- cbind(NV, NR)
colnames(df)[1:2] <- c("Blood_NV", "Tumour_NV")
colnames(df)[6:7] <- c("Blood_NR", "Tumour_NR")
df$tumour_vaf <- unlist(df$Tumour_NV)/unlist(df$Tumour_NR)
df$blood_vaf <- unlist(df$Blood_NV)/unlist(df$Blood_NR)

# filter out homozygous 
df <- df[df$blood_vaf < 0.7 & df$blood_vaf > 0.3 ,]#& (df$tumour_vaf > 0.6 | df$tumour_vaf < 0.4),] 
df <- na.omit(df)
chr <- rep(as.character(df$chr), 2)
pos <- as.numeric(rep(as.character(df$pos), 2))
df2 <- data.frame(chr  = chr,
                 pos =  pos,
                 vaf = c(unlist(df$tumour_vaf),unlist(df$blood_vaf)),
                 nr  = c(unlist(df$Tumour_NR),unlist(df$Blood_NR)),
                 sample = c(rep("Tumour",nrow(df)), rep("Blood",nrow(df))))
df2$chr <- factor(df2$chr, levels = paste("chr", 1:22, sep = ""))
df2$vaf[df2$vaf < 0.6 & df2$vaf > 0.4] <- 0.5
# df2$vaf <- 0.5-abs(df2$vaf - 0.5)
df2$vaf[df2$vaf > 0.8] <- 1
df2$vaf[df2$vaf < 0.2] <- 0
head(df2)

for(i in unique(df2$chr))
{
  df2$pos[df2$chr == i] <- (df2$pos[df2$chr == i]-min(df2$pos[df2$chr == i]))/(max(df2$pos[df2$chr == i])-min(df2$pos[df2$chr == i]))
}

VAF point plot: blood v.s. tumour

ggplot(df2[!is.nan(df2$vaf),], aes(x = pos, y = sample)) + 
  geom_point(aes(col = vaf, size = nr)) + 
  facet_wrap( ~ chr , ncol = 22)+
  ggplot2::scale_size_continuous(range = c(0, 6))+
  scale_colour_gradient2(mid="yellow", low = "turquoise", high = "red", midpoint=0.5)+
  ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(),panel.background = ggplot2::element_blank(),
                 axis.text.x=ggplot2::element_blank(),axis.title.x=ggplot2::element_blank(),axis.ticks.x=ggplot2::element_blank(),legend.position="none",
                 panel.border = ggplot2::element_rect(fill = NA, linetype = "solid", colour = "black"),plot.title = ggplot2::element_text(hjust = 0.5) ) 

ggplot(df2[df2$chr %in% c("chr2","chr6","chr13","chr15","chr17","chr22") & (!is.nan(df2$vaf)),], aes(x = pos, y = sample)) + 
  geom_point(aes(col = vaf, size = nr)) + 
  facet_wrap( ~ chr , ncol = 22)+
  ggplot2::scale_size_continuous(range = c(0, 10))+
  scale_colour_gradient2(mid="yellow", low = "turquoise", high = "red", midpoint=0.5)+
  ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(),panel.background = ggplot2::element_blank(),axis.text.x=ggplot2::element_blank(),axis.title.x=ggplot2::element_blank(),axis.ticks.x=ggplot2::element_blank(),
                    legend.position="bottom",panel.border = ggplot2::element_rect(fill = NA, linetype = "solid", colour = "black"),plot.title = ggplot2::element_text(hjust = 0.5))

ggplot(df2[df2$chr %in% c("chr2") & (!is.nan(df2$vaf)),], aes(x = pos, y = sample)) + 
  geom_point(aes(col = vaf, size = nr)) + 
  facet_wrap( ~ chr , ncol = 22)+
  ggplot2::scale_size_continuous(range = c(0, 15))+
  scale_colour_gradient2(mid="yellow", low = "turquoise", high = "red", midpoint=0.5)+
  ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(),panel.background = ggplot2::element_blank(),
                 axis.text.x=ggplot2::element_blank(),axis.title.x=ggplot2::element_blank(),axis.ticks.x=ggplot2::element_blank(),legend.position="bottom",
                 panel.border = ggplot2::element_rect(fill = NA, linetype = "solid", colour = "black"),
                 plot.title = ggplot2::element_text(hjust = 0.5)) 

ggplot(df2[df2$chr %in% c("chr6") & (!is.nan(df2$vaf)),], aes(x = pos, y = sample)) + 
  geom_point(aes(col = vaf, size = nr)) + 
  facet_wrap( ~ chr , ncol = 22)+
  ggplot2::scale_size_continuous(range = c(0, 15))+
  scale_colour_gradient2(mid="yellow", low = "turquoise", high = "red", midpoint=0.5)+
  ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(),panel.background = ggplot2::element_blank(),
                 axis.text.x=ggplot2::element_blank(),axis.title.x=ggplot2::element_blank(),axis.ticks.x=ggplot2::element_blank(),legend.position="bottom",
                 panel.border = ggplot2::element_rect(fill = NA, linetype = "solid", colour = "black"),
                 plot.title = ggplot2::element_text(hjust = 0.5)) 

17 cells with TP53 mutations

For these cells, we can see lose of chr6q, chr13, chr15, chr 17 and chr 22.

library(TxDb.Hsapiens.UCSC.hg19.knownGene) ## in order to map SNPs to genes

## Loading required package: GenomicFeatures

## Loading required package: AnnotationDbi

txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
hb <- new('HoneyBADGER', name='P11528')
hb$setAlleleMats(r.init=r[,r["chr17:7577515-7577515",] > 0],
                 n.sc.init=cov.sc[, r["chr17:7577515-7577515",] > 0], 
                 filter = F,
                 het.deviance.threshold=0.1, n.cores = 4) # 17 cells with mutated TP53 > 0

## Initializing allele matrices ... 
## Creating in-silico bulk ... 
## using 17 cells ... 
## Setting composite lesser allele count ... 
## Done setting initial allele matrices!

hb$setGeneFactors(txdb) ## map SNPs to genes

## Mapping snps to genes ... 
## >> preparing features information...      2018-07-12 14:27:33 
## >> identifying nearest features...        2018-07-12 14:27:34 
## >> calculating distance from peak to TSS...   2018-07-12 14:27:34 
## >> assigning genomic annotation...        2018-07-12 14:27:34 
## >> assigning chromosome lengths           2018-07-12 14:27:54 
## >> done...                    2018-07-12 14:27:54 
## Done mapping snps to genes!

hb$plotAlleleProfile() ## visualize individual SNPs

## Loading required package: reshape2

## Loading required package: gridExtra

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:Biobase':
## 
##     combine

## The following object is masked from 'package:BiocGenerics':
## 
##     combine

hb$plotAlleleProfile(chr = c("chr2","chr6","chr13","chr15","chr17","chr22")) ## visualize individual SNPs

13 cells without TP53 mutation (but have coverage on this loci)

The cells without mutation do not have obvious CNVs on chromosomal level.

hb2 <- new('HoneyBADGER', name='P11528')
hb2$setAlleleMats(r.init=r[,cov.sc["chr17:7577515-7577515",] > 0 & r["chr17:7577515-7577515",] == 0],
                 n.sc.init=cov.sc[,cov.sc["chr17:7577515-7577515",] > 0 & r["chr17:7577515-7577515",] == 0], filter = F,
                 het.deviance.threshold=0.1, n.cores = 4)

## Initializing allele matrices ... 
## Creating in-silico bulk ... 
## using 13 cells ... 
## Setting composite lesser allele count ... 
## Done setting initial allele matrices!

hb2$setGeneFactors(txdb) ## map SNPs to genes

## Mapping snps to genes ... 
## >> preparing features information...      2018-07-12 14:27:59 
## >> identifying nearest features...        2018-07-12 14:27:59 
## >> calculating distance from peak to TSS...   2018-07-12 14:27:59 
## >> assigning genomic annotation...        2018-07-12 14:27:59 
## >> assigning chromosome lengths           2018-07-12 14:28:01 
## >> done...                    2018-07-12 14:28:01 
## Done mapping snps to genes!

hb2$plotAlleleProfile() ## visualize individual SNPs

hb2$plotAlleleProfile(chr = c("chr2","chr6","chr13","chr15","chr17","chr22")) ## visualize individual SNPs

194 cells without coverage on the muated tp53 loci

hb3 <- new('HoneyBADGER', name='P11528')
hb3$setAlleleMats(r.init=r[,cov.sc["chr17:7577515-7577515",] == 0],
                 n.sc.init=cov.sc[,cov.sc["chr17:7577515-7577515",] == 0], filter = F,
                 het.deviance.threshold=0.1, n.cores = 4)

## Initializing allele matrices ... 
## Creating in-silico bulk ... 
## using 194 cells ... 
## Setting composite lesser allele count ... 
## Done setting initial allele matrices!

# 4536 heterozygous SNPs identified 
hb3$setGeneFactors(txdb) ## map SNPs to genes

## Mapping snps to genes ... 
## >> preparing features information...      2018-07-12 14:28:09 
## >> identifying nearest features...        2018-07-12 14:28:09 
## >> calculating distance from peak to TSS...   2018-07-12 14:28:09 
## >> assigning genomic annotation...        2018-07-12 14:28:09 
## >> assigning chromosome lengths           2018-07-12 14:28:11 
## >> done...                    2018-07-12 14:28:11 
## Done mapping snps to genes!

hb3$plotAlleleProfile() ## visualize individual SNPs

hb3$plotAlleleProfile(chr = c("chr2","chr6","chr13","chr15","chr17","chr22")) ## visualize individual SNPs

Introduce expression information

T-SNE plots of 11528 FTE cells

sce11528 <- readRDS("../expr/p11528_FTE_SingleCellExp_object.rds")
dim(sce11528)

## [1] 22110   258

sce11528 <- sce11528[,sce11528$Sample%in% cellinfo$Sample]
scater::plotTSNE(sce11528, colour_by = "initial.cluster")

These cells are filtered to be EpCAM+ epithelium cells

# logcounts(sce) <- log1p(calculateCPM(sce))
scater::plotTSNE(sce11528, colour_by = "KRT7", size_by =  "CAPS")

sce11528$p53mut.cov <- cov.sc["chr17:7577515-7577515",]
sce11528$p53mut.nv <- r["chr17:7577515-7577515",]

Some cells in cluster 11528L.2 and 11528L.3 have p53 mutations.

table(sce11528$p53mut.nv > 0,sce11528$p53mut.cov > 0, sce11528$initial.cluster)

## , ,  = 11528L.1
## 
##        
##         FALSE TRUE
##   FALSE     3    0
##   TRUE      0    0
## 
## , ,  = 11528L.2
## 
##        
##         FALSE TRUE
##   FALSE    31    0
##   TRUE      0   14
## 
## , ,  = 11528L.3
## 
##        
##         FALSE TRUE
##   FALSE    51    1
##   TRUE      0    3
## 
## , ,  = 11528L.4
## 
##        
##         FALSE TRUE
##   FALSE    94   10
##   TRUE      0    0
## 
## , ,  = 11528L.5
## 
##        
##         FALSE TRUE
##   FALSE    15    2
##   TRUE      0    0

sce11528$tp53_mut <- "no_cov"
sce11528$tp53_mut[sce11528$p53mut.nv > 0 ] <- "mut"
sce11528$tp53_mut[sce11528$p53mut.nv == 0 & sce11528$p53mut.cov > 0] <- "no-mut"

T-SNE plot by the mutation status of p53

plotTSNE(sce11528[, sce11528$tp53_mut!= "no_cov"], colour_by = "tp53_mut", shape_by = "initial.cluster")

plotTSNE(sce11528, colour_by = "p53mut.nv", size_by =  "p53mut.cov")

plotTSNE(sce11528[,sce11528$initial.cluster %in% c("11528L.2","11528L.3")], colour_by = "CUL7", size_by = "p53mut.nv", shape_by = "initial.cluster")

Calling CNVs from expression data

require(biomaRt) ## for gene coordinates

## Loading required package: biomaRt

mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
ft <- logcounts(sce11528)
# ref <- log1p(rowMeans(counts(sce11528)[,sce11528$initial.cluster == "11528L.4"])/1e6)
ref <- rowMeans(counts(sce11528) [,sce11528$initial.cluster %in% c("11528L.4","11528L.3")])
ref <- log1p(ref/sum(ref) * 1e5)
keep <- rowSums(ft > 1) > 20
ref <- ref[keep]
ft <- ft[keep,]

Epression CNVs of p53 mutated cells

# gexp.mats <- setGexpMats(ft, refFT, mart.obj, filter=FALSE, scale=T)
hb$setGexpMats(ft[,sce11528$p53mut.nv > 0], ref, mart.obj, filter=F, scale=T, verbose=F)

hb$plotGexpProfile(zlim = c(-1.2,1.2), window.size = 201)

They are LOH on chr13, 15, 17 and 22.

hb$plotAlleleProfile()

hb$plotAlleleProfile(chrs = "chr6")

colnames(ft[,sce11528$p53mut.nv > 0])

##  [1] "11528L-P2-H03" "11528L-P3-B02" "11528L-P4-E07" "11528L-P4-C02"
##  [5] "11528L-p1-L23" "11528L-p1-N21" "11528L-p2-A03" "11528L-p2-B01"
##  [9] "11528L-p2-N02" "11528L-p2-O04" "11528L-p1-C16" "11528L-p1-C03"
## [13] "11528L-p1-E24" "11528L-p1-J05" "11528L-p1-K13" "11528L-p1-N08"
## [17] "11528L-p1-O08"

Cells with tp53 mutation but in 11528L.3 clusters

These cells may be misclassified by clustering algorithm.

sce11528$Sample[sce11528$p53mut.nv > 0 & sce11528$initial.cluster == "11528L.3"]

## [1] "11528L-p2-O04" "11528L-p1-C16" "11528L-p1-N08"

hb4 <- new('HoneyBADGER', name='P11528')

hb4$setGexpMats(ft[,c("11528L-p2-O04","11528L-p1-C16","11528L-p1-N08")], ref, mart.obj, filter=F, scale=T, verbose=TRUE)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 3 cells ... 
## Done setting initial expression matrices!

hb4$plotGexpProfile(zlim = c(-1.5,1.5), window.size = 201)

hb4$setAlleleMats(r.init=r[,c("11528L-p2-O04","11528L-p1-C16","11528L-p1-N08")],
                 n.sc.init=cov.sc[, c("11528L-p2-O04","11528L-p1-C16","11528L-p1-N08")], 
                 filter = F,
                 het.deviance.threshold=0.1, n.cores = 4) # 17 cells with mutated TP53 > 0

## Initializing allele matrices ... 
## Creating in-silico bulk ... 
## using 3 cells ... 
## Setting composite lesser allele count ... 
## Done setting initial allele matrices!

hb4$setGeneFactors(txdb) ## map SNPs to genes

## Mapping snps to genes ... 
## >> preparing features information...      2018-07-12 14:28:52 
## >> identifying nearest features...        2018-07-12 14:28:52 
## >> calculating distance from peak to TSS...   2018-07-12 14:28:52 
## >> assigning genomic annotation...        2018-07-12 14:28:52 
## >> assigning chromosome lengths           2018-07-12 14:28:54 
## >> done...                    2018-07-12 14:28:54 
## Done mapping snps to genes!

hb4$plotAlleleProfile() ## visualize individual SNPs

sce11528$initial.cluster[sce11528$tp53_mut == "no-mut"]

##  [1] "11528L.4" "11528L.4" "11528L.5" "11528L.3" "11528L.4" "11528L.4"
##  [7] "11528L.4" "11528L.4" "11528L.4" "11528L.4" "11528L.5" "11528L.4"
## [13] "11528L.4"

hb2$setGexpMats(ft[,sce11528$tp53_mut == "no-mut"], ref, mart.obj, filter=F, scale=T, verbose=TRUE)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 13 cells ... 
## Done setting initial expression matrices!

hb2$plotGexpProfile(zlim = c(-1.5,1.5), window.size = 201)

hb3$setGexpMats(ft[,sce11528$tp53_mut == "no_cov"], ref, mart.obj, filter=F, scale=T, verbose=TRUE)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 194 cells ... 
## Done setting initial expression matrices!

hb3$plotGexpProfile(zlim = c(-1.5,1.5), window.size = 201)

hb5 <- new('HoneyBADGER', name='P11528')

hb5$setGexpMats(ft[,sce11528$tp53_mut == "no_cov"&sce11528$initial.cluster == "11528L.2"], ref, mart.obj, filter=F, scale=T, verbose=TRUE)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 31 cells ... 
## Done setting initial expression matrices!

hb5$plotGexpProfile(zlim = c(-1.2,1.2), window.size = 201)

11528L.3 no coverage

hb5 <- new('HoneyBADGER', name='P11528')
hb5$setGexpMats(ft[,sce11528$tp53_mut == "no_cov"&sce11528$initial.cluster == "11528L.3"], ref, mart.obj, filter=F, scale=T, verbose=F)
hb5$plotGexpProfile(zlim = c(-1.2,1.2), window.size = 201)

gexp.mats <- setGexpMats(ft, ref, mart.obj, filter=FALSE, scale=T)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 224 cells ... 
## Done setting initial expression matrices!

gexp.plot.all <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes,window.size = 201,zlim = c(-1.2, 1.2))

An example with chr6p amp

plot(1:396,gexp.plot.all$tlsmooth[[6]][,"11528L-p1-O08"])

An example with no chr6p amp

plot(1:396,gexp.plot.all$tlsmooth[[6]][,"11528L-P1-C01"])

Score cells based on the chr6p amplification

100 = amplified

0 = no amplified

hist(colSums(gexp.plot.all$tlsmooth[[6]][100:200,] > 0.1),30)

48 cells with strong amp signal at chr6p:

gexp.mats <- setGexpMats(ft[,names(which(colSums(gexp.plot.all$tlsmooth[[6]][100:200,] > 0.1) > 50))], ref, mart.obj, filter=FALSE, scale=T)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 48 cells ... 
## Done setting initial expression matrices!

gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes,window.size = 201,zlim = c(-1.2, 1.2))

161 cells without signals:

gexp.mats <- setGexpMats(ft[,names(which(colSums(gexp.plot.all$tlsmooth[[6]][100:200,]> 0.1) < 10))], ref, mart.obj, filter=FALSE, scale=T)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 161 cells ... 
## Done setting initial expression matrices!

gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes,window.size = 201,zlim = c(-1.2, 1.2))

15 cells with dodge signals

gexp.mats <- setGexpMats(ft[,names(which(colSums(gexp.plot.all$tlsmooth[[6]][100:200,] > 0.1) >= 10 & colSums(gexp.plot.all$tlsmooth[[6]][100:200,] > 0.1) <=50))], ref, mart.obj, filter=FALSE, scale=T)

## Initializing expression matrices ... 
## Scaling coverage ... 
## Normalizing gene expression for 8465 genes and 15 cells ... 
## Done setting initial expression matrices!

gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes,window.size = 201,zlim = c(-1.2, 1.2))

sce11528$chr6pamp <- colSums(gexp.plot.all$tlsmooth[[6]][100:200,] > 0.1)
table(sce11528$chr6pamp, sce11528$initial.cluster)

##      
##       11528L.1 11528L.2 11528L.3 11528L.4 11528L.5
##   0          2        1       31      104       14
##   2          1        1        0        0        1
##   3          0        0        1        0        0
##   4          0        0        1        0        0
##   5          0        2        0        0        0
##   7          0        0        1        0        0
##   8          0        0        1        0        0
##   11         0        0        1        0        0
##   13         0        0        1        0        0
##   14         0        1        2        0        0
##   16         0        1        0        0        0
##   17         0        0        1        0        0
##   19         0        0        0        0        1
##   23         0        1        0        0        0
##   35         0        1        0        0        0
##   37         0        2        0        0        0
##   38         0        1        1        0        0
##   40         0        0        1        0        0
##   57         0        0        1        0        0
##   59         0        0        1        0        0
##   66         0        1        0        0        0
##   78         0        0        0        0        1
##   80         0        1        0        0        0
##   86         0        1        0        0        0
##   94         0        0        1        0        0
##   97         0        1        0        0        0
##   100        0        0        1        0        0
##   101        0       30        9        0        0

scater::plotTSNE(sce11528, colour_by = "chr6pamp", shape_by = "tp53_mut")

# jpeg(filename = "../plots/20180704HoneyBadger11528/tp53mut_ampChr6_11528FTE.jpg", res = 150,width = 10, height = 8, units = "in")
# scater::plotTSNE(sce11528, colour_by = "chr6pamp", shape_by = "tp53_mut")
# dev.off()

scater::plotPCA(sce11528, colour_by = "chr6pamp", shape_by = "tp53_mut")

# jpeg(filename = "../plots/20180704HoneyBadger11528/tp53mut_ampChr6_11528FTE2.jpg", res = 150,width = 10, height = 8, units = "in")
# scater::plotTSNE(sce11528, size_by = "chr6pamp", colour_by = "tp53_mut")
# dev.off()

ggplot(data.frame(sce11528@colData), aes(x = initial.cluster, y = chr6pamp)) +  geom_point(aes(col = tp53_mut),position=position_jitterdodge()) + theme_classic()

Can we find mutations with chr6 alteration and mutations

100 somatic mutations in the tumour: how many of them we find in single cell data